To the extent that I have thought about this area (not deeply), I suspect that clinical models for these types of use probably should take the form of templates that 'mix in' multiple bits and pieces from existing clinical archetypes.
A complex of data elements that you might expect from a (say) person on a rowing machine will be some typical vital signs (breathing, heart rate, O2 sat, real time blood analytes?, maybe some computed derivatives, vO2 etc) - which will mostly already exist (maybe not vO2) PLUS a bunch of machine parameters, like resistance, settings, etc. These latter almost certainly don't have archetypes today, but making them should not hard for those who work with this kind of data.
The point is that we should use templates to create the somewhat ad hoc data mashups that will result from these kinds of measurements. Even just pedometer + heart data will be a small mashup of heart rate, steps taken, GPS location etc. If you decide you need a new mashup, just specialise the template, or throw it away and make a new one. The data will all be reliable, assuming your underlying archetypes are relatively stable.
This is not quite what I said in the previous discussion, but it seems to me the more likely way of doing the modelling. The reason for somewhat ad hoc nature of such templates is that the data groups are probably not as scientific as illness related classic healthcare ones.
Others may have better ideas, interested to hear from anyone who works with this kind of data.
- thomas